With the acceleration of the global response to climate change, China
announced to the world in 2020 the goals of carbon peaking by 2030 and
carbon neutrality by 2060, which reflects its firm determination implement
Intended Nationally Determined Contributions. Energy transition is the key
to achieving the carbon peaking and carbon neutrality goals. It is of great
theoretical and practical significance to study the impact of low carbon
energy transition on China?s macro-economy under the carbon peaking and
carbon neutrality goals. Based on three carbon neutrality scenarios, this
paper uses the dynamic computable general equilibrium model to simulate and
estimate the impact of different energy transition pathways on China?s
macro-economy. The results show that under the carbon peaking and carbon
neutrality target, accelerating energy transition will have a certain
nega?tive impact on China?s economic growth, and different energy transition
pathways have different impacts on the macro-economy. The sustainable
transformation scenario that promotes energy transformation through the
carbon tax policy to adjust carbon emission intensity and the renewable
energy incentive policy to reduce costs has the least negative impact on the
macro-economy, promoting employment growth, and optimizing industrial
structure to a certain extent in the process of energy transformation. In
conclusion, relevant policy recommendations are put forward for the
achievement of the carbon peaking and carbon neutrality goals, and the
promotion of high quality economic development.
In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA–LSTM model were compared with those of other models. The results showed that the IGA–LSTM model could effectively capture the characteristics and trends of time-series changes. The results showed that the proposed model obtained the minimum values (MSE = 0.00107, RMSE = 0.03268, and MAPE = 0.0691) in the forecasting of futures prices for two types of chemical products, showing excellent forecasting performance.
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